Like the chicken and the egg conundrum, it may seem there is no right answer. Your business can’t use analytics effectively without quality data to input, but you’ll never have clean data without analytics to expose the data errors.

However, the real problem is that there is no such thing as clean sales data. It’s a myth. Your data is dirty, and so is everyone else’s. In sales, a team of reps is entering new data into a CRM each day. Inevitably, some of that data will be slightly inaccurate — and that’s OK.

While you do need to collect sales data in order to analyze it, it doesn’t have to be 100% clean, pristine, perfect data. It’s how you handle the data going forward that makes the biggest difference in your ability to access accurate analytics. Your sales team must have a constantly evolving process in place to clean your data, improve your data, and drive better data performance.

In fact, the chicken and the egg question doesn’t really matter. What does matter is how you train your sales team to manage data quality today. With the right processes in place, you can take advantage of analytics even without the unicorn of perfectly clean sales data.

Those mistakes compound over time, and eventually become unmanageable. After months or years of data errors, you may feel completely overwhelmed by the prospect of trying to clean your sales data — but you have to start somewhere.

While it may take a while to go back and clean your historical data, today is the day to implement a new process for cleaning your data in the past, and going forward. The first step: who owns data quality on your team?

You need to have one source of truth.

Lauren Kelley
OPEXEngine

At some companies, data quality is owned by sales; on other teams, it’s owned by marketing operations or finance. It really depends on your company’s organizational structure, and what works best for your team, according to sales operations expert, Lauren Kelley. She explained that as SaaS companies grow, this decision becomes even more vital.

“The SaaS model has many more moving pieces than a traditional product sales model,” she noted. “Those moving pieces need to be coordinated and you need to have one source of truth in terms of what the numbers are, so that the pipeline, the forecast, the billing, the invoicing, and the cash of the company are all coming from one source of data.”

Kelly recommends that sales operations owns data quality, but that the role sits on the finance team in order to integrate all the disparate systems. However, the important part is simply making the decision and giving one team the responsibility for data quality. Once you have sales ops in place on your team, set measurable goals for data quality. With the authority to enforce the rules across the sales team and goals to drive them, you’ll see a rapid push to improve your sales data.

Analyze Your Current Data

Now that you have a leader focused on data quality, it’s time to delve into the data. What’s in your CRM today?

It’s impossible to know what your team needs to improve until you understand the exact challenges you’re facing. Do you have major gaps in data? Missing fields? Inaccurate numbers, or information that isn’t in the right format? If so, that’s the first thing you need to improve. For Jonathan Bunford, Sales Operations Manager at Influitive, analytics shone a light on their database, revealing exactly where it was dirty.

“Analytics helped highlight the need for our data to be clean, otherwise our tracking and forecasting is unreliable,” he explained. “It’s easy to realize you have a problem when you find yourself tripping over duplicates within your database.”

Bunford closely examined the sales process on the team, outlining what reps did to input data into the CRM. He recommended that every sales team start improving data quality by training reps on the correct processes, and pushing stronger enforcement of data quality right away.

“Start small,” he advised. “We all struggle with unclean data within our database and if you try to clean it all at once you’ll likely find it too overwhelming. Start with one object and move forward at a steady pace.”

Analytics highlighted the need for our data to be clean.

Jonathan Bunford
Influitive

The project may take months to complete, but eventually and incrementally, your historical data quality will improve. Make sure the sales ops team is slowly but surely working toward specific goals, and progress will be made.

Create Ongoing Data Improvements

Even as your historical data quality starts to improve, you can never become complacent or smug. Data quality can easily and quickly go downhill if you don’t implement an ongoing process to continually improve it in the future. In fact, your data quality process should never be static. Clark Bakstran, Account Executive at InsightSquared, has talked to thousands of sales leaders at SaaS companies, and has seen the same pattern repeated again and again.

Moments of pristine, accurate data-quality should always be short lived.

Clark Bakstran
InsightSquared

“In the world of high-growth tech companies, moments of pristine, accurate data-quality should always be short lived,” Bakstran said. “Every company should always be changing their Salesforce process. If you’re not changing your process at least twice a year, you’re going to fail as a company.”

Think about the gaps in your CRM today. What data would you love to have access to, but don’t? How would that information make a difference to your business, if you were able to analyze it? For example, if you want to understand how company size affects your sales team’s win rate, you have to have reliable data on company size. If that data is unreliable or nonexistent, it’s impossible to get accurate reporting.

Eric Riviera, Marketing Operations Manager at Chute, said this is exactly what happened on his team. They needed more information around push rates, opportunity changes and loss reasons. However, they didn’t have the information in the CRM at the time to track that information. Rather than give up, he helped create a completely new sales process to build out that data in the CRM.

“We built great workflows that encourage and track the right behaviors,” Riviera explained. “Then, we enforced the workflows from the top down.”

The best companies usually follow a 6 to 8 month iterative cycle that looks something like this:

Train sales reps in new data process (2 – 4 weeks)

Figure out where reps are failing to follow new process, and re-train/clean data (2 – 3 months)

Evaluate pros/cons of a new sales process (1 – 2 months, usually started during step 2)

Develop new process (1 – 2 months)

Roll out new process (1 month)

Repeat Step 1

It might sound like a simple enough process, but there is a ton of tough work hidden in the details of each step. Your data is actually at its cleanest during steps 4 and 5. However, that’s not the most important part of the process. Step 3 is where you make the most important and strategic decision in regard to the future of your sales team. It’s at this point that analytics can be most useful to your team, and help you choose where your database has gaps, and what information your team needs the most.

As you can see, step 6 is to repeat the process again. This entire workflow is iterative and agile, and it’s never finished. Once a new sales process is complete, you should always start on the next item on the list to improve. For example, after company size is accurate, it’s time to focus on industry data. This process never ends, and should lead to continuous data improvements on your sales team.

Get Started Today

Achieving a higher level of data quality may seem a little overwhelming, especially if your sales data is in especially rough shape. However, waiting will not make your problem any better. Robin Danahy, Senior Sales Administrator at Orion Advisor Services, explained that she went through this difficult process herself, and she understands exactly why it can be daunting to start.

“Do not wait!” Danahy emphasized. “Do your clean up now before it becomes completely out of hand. If you feel your data is too far gone, there is still hope.”

Danahy’s team did a massive cleanup effort that took months to complete, she said, but the work has fully paid off. With an analytics solution in place to help her understand exactly where the data quality was lacking, Dahany made targeted changes to drive the right improvements in the data.

“Analytics helps to make sure that no one falls through the cracks like before,” Danahy said. “It forces the team to ensure the data is up-to-date so reporting is accurate. It was completely worth it, and has helped us close more deals and shorten our sales cycle.”